The invention relates to a circuit arrangement or a method for synchronizing clocks in a network comprising a plurality of at least two nodes, wherein at least two of these nodes can communicate with each other and each have a local clock.
High-precision synchronization of networks is necessary in many new systems to allocate unambiguous time points on a common time scale to various events. This must be possible even if the events occur on different stations or at different nodes. For time division multiplex operation, a common time base is necessary, in particular, to be able to properly separate various channels from each other.
In order to achieve high-precision synchronization, clock sources have been necessary which provide a very precisely known frequency. The requirements as to serial scattering and drift cause substantial cost. The clock sources must be configured in such a way that they only slightly drift from their frequency in the course of ageing and also in dependence on varying ambient conditions, such as varying temperature.
As a result, relatively large guard intervals have been used in TDMA (Time Division Multiple Access) networks to ensure proper channel separation despite suboptimal synchronicity. On the one hand this either leads to poor channel utilization or to very long time slots, and on the other hand to unnecessarily long operating periods for the receiver, since the receiver must already be operating during the guard interval.
If a network is used for coordinating actuators and sensors, there are also frequently real time requirements to be able to control or feedback-control complex systems in a time synchronous manner, or to allocate a precise time stamp to measuring values. A requirement as to the synchronization of actuator and/or sensor networks exists, in particular, in modern distributed measuring and feedback-control systems, such as in automation technology, vehicle technology, building technology or robotics.
In radio location systems, synchronizing the network nodes is also a central task. Simple signal delay measurements are only possible if precisely the same times are present in all stations involved in the measurement. In GPS (Global Positioning System) atomic clocks are used, for example, in satellites, or in other location systems complex synchronization methods are used to ensure clock synchronicity.
Both in measuring and communication systems, often coded or code-spread transmission signals and correlating receivers are used to improve signal-to-noise ratio in the receiver. Frequently used signal forms, also referred to as spread spectrum signals, are, for example, pseudo-random phase or amplitude-modulated pulse sequences or linearly or stepwise frequency-modulated signals. It is well known that it is very advantageous in such correlating systems if the transmitter and the receiver are pre-synchronized, since then the correlator is much simpler to realize, i.e., with smaller computing overhead in software or with simpler hardware correlators, and/or the time duration needed for correlation can be reduced.
Current approaches to solve the problems of synchronization have usually been based on having one station or one node generate locally a clock or a time scale, which is then provided to the other stations by means of the synchronization protocol. This local time scale is then established as a global time scale in the manner of a master-slave system.
One problem that remains in this approach is the stability and availability of the global time scale should individual stations fail. Serious limitations with respect to the quality of synchronization arise in radio networks, in particular, between mobile network nodes.
Well-known methods for synchronizing network nodes are, for example, NTP (Network Time Protocol), PTP (Precision Time Protocol), TPSN (Timing-Sync Protocol for Sensor Networks), or FTSP (Flooding Time Synchronization Protocol).
NTP (Network Time Protocol) is based on manually allocated strata resulting in a top-down tree structure in the allocation of time, as described in Mills, D.: Network Time Protocol (Version 3) Specification, Implementation and Analysis. RFC 1305 (Draft Standard), University of Delaware, Version March 1992. One specific aspect herein is that prior to leap seconds, two time scales are maintained for a period of time—one for the time period up to the leap second, and one for the time period following the leap second. In this manner, an NTP synchronized network is capable of providing even non-continuous timing processes in a synchronized manner.
PTP (Precision Time Protocol) utilizes the so-called best master clock protocol to determine the best available clock, as is described in IEEE 1588: Standard for a Precision Clock Synchronization Protocol for Networked Measurement and Control Systems. Subsequently, the time is distributed in a similar manner as with NTP.
TPSN (Timing-Sync Protocol for Sensor Networks) describes a tree structure among nodes able to communicate with each other, and here distributes the time scale of that node involved having the lowest serial number, as is well known from Ganeriwal, Saurabh; Kumar, Ram; Srivastava, Mani B., Timing-sync protocol for sensor networks, SenSys '03: Proceedings of the 1st international conference on embedded networked sensor systems, New York, ACM Press, 2003.
FTSP (Flooding Time Synchronization Protocol) has a master in each cohesive tree, which is determined by the fact that it has the lowest serial number, as is known from Maroti M., Kusy B., Simon G., Ledeczi A.: The Flooding Time Synchronization Protocol, ISIS-04-501, 12 Feb. 2004.
The previously mentioned protocols have in common that failure or unavailability of a master must be detected and a new master must be determined as quickly as possible whose time scale must be adapted. This process is highly time-critical since with a desired synchronicity of 10 μs, for example, and a quartz clock difference of 40 ppm between various nodes, synchronicity is lost as early as after 0.25 s, when a master becomes unavailable, and a portion of the slaves already follows the new master, while other slaves continue to extrapolate the time scale of the old master.
To achieve high-precision synchronicity of such a structure, the intervals between individual reference measurements must be held very short. This leads to high channel loading due to synchronization alone, and to substantial basic power expenditure in mobile nodes.
While FTSP alleviates this problem by causing one node that switches from slave to master to initially extrapolate and therefore to imitate the time scale of the previous master, so that the stations of a network only slowly drift apart if the master is lost, the time of a plurality of measuring intervals is still needed, however, before the loss of a master is detected and the new master is determined.
The use of a distributed Kalman filter in networks is well known from Olfati-Saber: Distributed Kalman Filter with Embedded Consensus Filters, in: CDC-ECC '05: 12-15 Dec. 2005, pp. 8179-8184.
If a well-known distributed Kalman filter were used for time estimation, for example, foreign sensor information would pass through the consensus filter and thereby be subject to delay. If the system equation of the distributed Kalman filter is represented asx′=Ax+Bu P′=APAT+Q′
it is necessary that ∥A−E∥2<ε applies to keep this systematic error, caused by the delay, small. This can be achieved by accelerating clocking to such an extent that the so-called propagation delay is short with respect to system dynamics. Herein, x is the old estimated system state, represented as a vector in the state space, and P is the covariance as a scattering parameter of the old system estimation. A is the system matrix of a typical Kalman filter. The vector u represents the intervention in the system, and the intervention effect matrix B describes the influence on the system state. Moreover, x′ and P′ are the new system state and the covariance of the new system estimation. Q is the covariance of the expected interferences. ε is a small constant depending on the requirements on filtering.
These distributed Kalman filters are not suitable, however, for use in synchronization processes, since Kalman filters can only use the sensor values of other stations in a manner affected by latency.
Other aspects of synchronization are well known from Roehr, Sven; Gulden, Peter; Vossiek, Martin: Method for High Precision Clock Synchronization in Wireless Systems with Application to Radio Navigation, pp. 551-554, Radio and Wireless Symposium, January 2007 IEEE.
Indications on the basic structure of TOA and TDOA systems may be found, for example, in M. Vossiek, L. Wiebking, P. Gulden, J. Weighardt, C. Hoffmann and P. Heide, “Wireless local positioning,” IEEE Microwave Magazine, vol. 4, pp. 77-86, 2003, or in DE1254206 or in DE 1240146, DE 1214754, U.S. Pat. No. 3,864,681 or U.S. Pat. No. 5,216,429, and in literature on the Global Positioning System GPS.